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<div class="title">regression_variance_stats_estimator.h</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Software License Agreement (BSD License)</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> *  Point Cloud Library (PCL) - www.pointclouds.org</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *  Copyright (c) 2010-2011, Willow Garage, Inc.</span></div>
<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> *  All rights reserved.</span></div>
<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment"> *  Redistribution and use in source and binary forms, with or without</span></div>
<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> *  modification, are permitted provided that the following conditions</span></div>
<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment"> *  are met:</span></div>
<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="comment"> *   * Redistributions of source code must retain the above copyright</span></div>
<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> *     notice, this list of conditions and the following disclaimer.</span></div>
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<div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;<span class="comment"> *  POSSIBILITY OF SUCH DAMAGE.</span></div>
<div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;<span class="comment"> */</span></div>
<div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;  </div>
<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;<span class="preprocessor">#ifndef PCL_ML_REGRESSION_VARIANCE_STATS_ESTIMATOR_H_</span></div>
<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;<span class="preprocessor">#define PCL_ML_REGRESSION_VARIANCE_STATS_ESTIMATOR_H_</span></div>
<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160; </div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="common_2include_2pcl_2common_2common_8h.html">pcl/common/common.h</a>&gt;</span></div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="preprocessor">#include &lt;pcl/ml/stats_estimator.h&gt;</span></div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;<span class="preprocessor">#include &lt;pcl/ml/branch_estimator.h&gt;</span></div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160; </div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="preprocessor">#include &lt;istream&gt;</span></div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;<span class="preprocessor">#include &lt;ostream&gt;</span></div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160; </div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;<span class="keyword">namespace </span>pcl</div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;{</div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160; </div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">class</span> FeatureType, <span class="keyword">class</span> LabelType&gt;</div>
<div class="line"><a name="l00053"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_node.html">   53</a></span>&#160;  <span class="keyword">class </span>PCL_EXPORTS <a class="code" href="classpcl_1_1_regression_variance_node.html">RegressionVarianceNode</a> </div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;  {</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;    <span class="keyword">public</span>:</div>
<div class="line"><a name="l00057"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_node.html#a0a97daa781ba92b787d65caf3c91c3ac">   57</a></span>&#160;      <a class="code" href="classpcl_1_1_regression_variance_node.html#a0a97daa781ba92b787d65caf3c91c3ac">RegressionVarianceNode</a> () : value(0), variance(0), threshold(0), sub_nodes() {}</div>
<div class="line"><a name="l00059"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_node.html#abaa10aed19a5efe2a0c836b1c4d0b0cb">   59</a></span>&#160;      <span class="keyword">virtual</span> <a class="code" href="classpcl_1_1_regression_variance_node.html#abaa10aed19a5efe2a0c836b1c4d0b0cb">~RegressionVarianceNode</a> () {}</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160; </div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00065"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_node.html#a24c9f71bfad5c728bf605f445174ef17">   65</a></span>&#160;      <a class="code" href="classpcl_1_1_regression_variance_node.html#a24c9f71bfad5c728bf605f445174ef17">serialize</a> (std::ostream &amp; stream)<span class="keyword"> const</span></div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;<span class="keyword">      </span>{</div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;        feature.serialize (stream);</div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160; </div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;        stream.write (<span class="keyword">reinterpret_cast&lt;</span><span class="keyword">const </span><span class="keywordtype">char</span>*<span class="keyword">&gt;</span> (&amp;threshold), <span class="keyword">sizeof</span> (threshold));</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160; </div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;        stream.write (<span class="keyword">reinterpret_cast&lt;</span><span class="keyword">const </span><span class="keywordtype">char</span>*<span class="keyword">&gt;</span> (&amp;value), <span class="keyword">sizeof</span> (value));</div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;        stream.write (<span class="keyword">reinterpret_cast&lt;</span><span class="keyword">const </span><span class="keywordtype">char</span>*<span class="keyword">&gt;</span> (&amp;variance), <span class="keyword">sizeof</span> (variance));</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160; </div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span> num_of_sub_nodes = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (sub_nodes.size ());</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;        stream.write (<span class="keyword">reinterpret_cast&lt;</span><span class="keyword">const </span><span class="keywordtype">char</span>*<span class="keyword">&gt;</span> (&amp;num_of_sub_nodes), <span class="keyword">sizeof</span> (num_of_sub_nodes));</div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> sub_node_index = 0; sub_node_index &lt; num_of_sub_nodes; ++sub_node_index)</div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;        {</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;          sub_nodes[sub_node_index].serialize (stream);        </div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;        }</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;      }</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160; </div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00086"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_node.html#a3463081addd9c9e2237f764e031b6d4e">   86</a></span>&#160;      <a class="code" href="classpcl_1_1_regression_variance_node.html#a3463081addd9c9e2237f764e031b6d4e">deserialize</a> (std::istream &amp; stream)</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;      {</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;        feature.deserialize (stream);</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160; </div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;        stream.read (<span class="keyword">reinterpret_cast&lt;</span><span class="keywordtype">char</span>*<span class="keyword">&gt;</span> (&amp;threshold), <span class="keyword">sizeof</span> (threshold));</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160; </div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;        stream.read (<span class="keyword">reinterpret_cast&lt;</span><span class="keywordtype">char</span>*<span class="keyword">&gt;</span> (&amp;value), <span class="keyword">sizeof</span> (value));</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;        stream.read (<span class="keyword">reinterpret_cast&lt;</span><span class="keywordtype">char</span>*<span class="keyword">&gt;</span> (&amp;variance), <span class="keyword">sizeof</span> (variance));</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160; </div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;        <span class="keywordtype">int</span> num_of_sub_nodes;</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;        stream.read (<span class="keyword">reinterpret_cast&lt;</span><span class="keywordtype">char</span>*<span class="keyword">&gt;</span> (&amp;num_of_sub_nodes), <span class="keyword">sizeof</span> (num_of_sub_nodes));</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;        sub_nodes.resize (num_of_sub_nodes);</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160; </div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;        <span class="keywordflow">if</span> (num_of_sub_nodes &gt; 0)</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;        {</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">int</span> sub_node_index = 0; sub_node_index &lt; num_of_sub_nodes; ++sub_node_index)</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;          {</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;            sub_nodes[sub_node_index].deserialize (stream);</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;          }</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;        }</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;      }</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160; </div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;    <span class="keyword">public</span>:</div>
<div class="line"><a name="l00110"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_node.html#a4b1410ff0469bde2394b843c70229556">  110</a></span>&#160;      FeatureType <a class="code" href="classpcl_1_1_regression_variance_node.html#a4b1410ff0469bde2394b843c70229556">feature</a>;</div>
<div class="line"><a name="l00112"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_node.html#a404fad6110f5fed14015839d59c6dca7">  112</a></span>&#160;      <span class="keywordtype">float</span> <a class="code" href="classpcl_1_1_regression_variance_node.html#a404fad6110f5fed14015839d59c6dca7">threshold</a>;</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160; </div>
<div class="line"><a name="l00115"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_node.html#a52ce572a1d9852122bd74c074b76c2e3">  115</a></span>&#160;      LabelType <a class="code" href="classpcl_1_1_regression_variance_node.html#a52ce572a1d9852122bd74c074b76c2e3">value</a>;</div>
<div class="line"><a name="l00117"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_node.html#a266146f39a982a8f1c1f167c29e1b63d">  117</a></span>&#160;      LabelType <a class="code" href="classpcl_1_1_regression_variance_node.html#a266146f39a982a8f1c1f167c29e1b63d">variance</a>;</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160; </div>
<div class="line"><a name="l00120"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_node.html#a84b352820a9f306fd303f5fca7792da0">  120</a></span>&#160;      std::vector&lt;RegressionVarianceNode&gt; <a class="code" href="classpcl_1_1_regression_variance_node.html#a84b352820a9f306fd303f5fca7792da0">sub_nodes</a>;</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;  };</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160; </div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">class</span> LabelDataType, <span class="keyword">class</span> NodeType, <span class="keyword">class</span> DataSet, <span class="keyword">class</span> ExampleIndex&gt;</div>
<div class="line"><a name="l00125"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_stats_estimator.html">  125</a></span>&#160;  <span class="keyword">class </span>PCL_EXPORTS <a class="code" href="classpcl_1_1_regression_variance_stats_estimator.html">RegressionVarianceStatsEstimator</a></div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;    : <span class="keyword">public</span> <a class="code" href="classpcl_1_1_stats_estimator.html">pcl::StatsEstimator</a>&lt;LabelDataType, NodeType, DataSet, ExampleIndex&gt;</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;  {</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;  </div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    <span class="keyword">public</span>:</div>
<div class="line"><a name="l00131"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_stats_estimator.html#a73f69f48a8a6593b47b34f93968cd1a9">  131</a></span>&#160;      <a class="code" href="classpcl_1_1_regression_variance_stats_estimator.html#a73f69f48a8a6593b47b34f93968cd1a9">RegressionVarianceStatsEstimator</a> (<a class="code" href="classpcl_1_1_branch_estimator.html">BranchEstimator</a> * branch_estimator) </div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;        : branch_estimator_ (branch_estimator)</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;      {}</div>
<div class="line"><a name="l00135"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_stats_estimator.html#a64941b7d4d7e4edd9262b18e4c36937a">  135</a></span>&#160;      <span class="keyword">virtual</span> <a class="code" href="classpcl_1_1_regression_variance_stats_estimator.html#a64941b7d4d7e4edd9262b18e4c36937a">~RegressionVarianceStatsEstimator</a> () {}</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;  </div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">size_t</span> </div>
<div class="line"><a name="l00139"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_stats_estimator.html#ae0a9c3defc64353f7053f4eb63edbc6d">  139</a></span>&#160;      <a class="code" href="classpcl_1_1_regression_variance_stats_estimator.html#ae0a9c3defc64353f7053f4eb63edbc6d">getNumOfBranches</a> ()<span class="keyword"> const </span></div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;<span class="keyword">      </span>{ </div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;        <span class="comment">//return 2; </span></div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;        <span class="keywordflow">return</span> branch_estimator_-&gt;getNumOfBranches ();</div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;      }</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160; </div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;      <span class="keyword">inline</span> LabelDataType </div>
<div class="line"><a name="l00149"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_stats_estimator.html#a3701ce301bd5fbd96aa7ee8ce6c2dc29">  149</a></span>&#160;      <a class="code" href="classpcl_1_1_regression_variance_stats_estimator.html#a3701ce301bd5fbd96aa7ee8ce6c2dc29">getLabelOfNode</a> (</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;        NodeType &amp; node)<span class="keyword"> const</span></div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;<span class="keyword">      </span>{</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;        <span class="keywordflow">return</span> node.value;</div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;      }</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160; </div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;      <span class="keywordtype">float</span> </div>
<div class="line"><a name="l00164"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_stats_estimator.html#a7c5c4efbb4ac50525954aae8b8389995">  164</a></span>&#160;      <a class="code" href="classpcl_1_1_regression_variance_stats_estimator.html#a7c5c4efbb4ac50525954aae8b8389995">computeInformationGain</a> (</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;        DataSet &amp; data_set,</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;        std::vector&lt;ExampleIndex&gt; &amp; examples,</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;        std::vector&lt;LabelDataType&gt; &amp; label_data,</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;        std::vector&lt;float&gt; &amp; results,</div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;        std::vector&lt;unsigned char&gt; &amp; flags,</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">float</span> threshold)<span class="keyword"> const</span></div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;<span class="keyword">      </span>{</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_examples = examples.size ();</div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_branches = getNumOfBranches();</div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160; </div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;        <span class="comment">// compute variance</span></div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;        std::vector&lt;LabelDataType&gt; sums (num_of_branches+1, 0);</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;        std::vector&lt;LabelDataType&gt; sqr_sums (num_of_branches+1, 0);</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;        std::vector&lt;size_t&gt; branch_element_count (num_of_branches+1, 0);</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160; </div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> branch_index = 0; branch_index &lt; num_of_branches; ++branch_index)</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;        {</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;          branch_element_count[branch_index] = 1;</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;          ++branch_element_count[num_of_branches];</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;        }</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160; </div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> example_index = 0; example_index &lt; num_of_examples; ++example_index)</div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;        {</div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;          <span class="keywordtype">unsigned</span> <span class="keywordtype">char</span> branch_index;</div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;          computeBranchIndex (results[example_index], flags[example_index], threshold, branch_index);</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160; </div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;          LabelDataType label = label_data[example_index];</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160; </div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;          sums[branch_index] += label;</div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;          sums[num_of_branches] += label;</div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160; </div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;          sqr_sums[branch_index] += label*label;</div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;          sqr_sums[num_of_branches] += label*label;</div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160; </div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;          ++branch_element_count[branch_index];</div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;          ++branch_element_count[num_of_branches];</div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;        }</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160; </div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;        std::vector&lt;float&gt; variances (num_of_branches+1, 0);</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> branch_index = 0; branch_index &lt; num_of_branches+1; ++branch_index)</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;        {</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;          <span class="keyword">const</span> <span class="keywordtype">float</span> mean_sum = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(sums[branch_index]) / branch_element_count[branch_index];</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;          <span class="keyword">const</span> <span class="keywordtype">float</span> mean_sqr_sum = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(sqr_sums[branch_index]) / branch_element_count[branch_index];</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;          variances[branch_index] = mean_sqr_sum - mean_sum*mean_sum;</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;        }</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160; </div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;        <span class="keywordtype">float</span> information_gain = variances[num_of_branches];</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> branch_index = 0; branch_index &lt; num_of_branches; ++branch_index)</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;        {</div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;          <span class="comment">//const float weight = static_cast&lt;float&gt;(sums[branchIndex]) / sums[numOfBranches];</span></div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;          <span class="keyword">const</span> <span class="keywordtype">float</span> weight = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(branch_element_count[branch_index]) / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(branch_element_count[num_of_branches]);</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;          information_gain -= weight*variances[branch_index];</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;        }</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160; </div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;        <span class="keywordflow">return</span> information_gain;</div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;      }</div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160; </div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;      <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00229"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_stats_estimator.html#a5de2e6cbeff519afd171414f66a953aa">  229</a></span>&#160;      <a class="code" href="classpcl_1_1_regression_variance_stats_estimator.html#a5de2e6cbeff519afd171414f66a953aa">computeBranchIndices</a> (</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;        std::vector&lt;float&gt; &amp; results,</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;        std::vector&lt;unsigned char&gt; &amp; flags,</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">float</span> threshold,</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;        std::vector&lt;unsigned char&gt; &amp; branch_indices)<span class="keyword"> const</span></div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;<span class="keyword">      </span>{</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_results = results.size ();</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_branches = getNumOfBranches();</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160; </div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;        branch_indices.resize (num_of_results);</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> result_index = 0; result_index &lt; num_of_results; ++result_index)</div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;        {</div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;          <span class="keywordtype">unsigned</span> <span class="keywordtype">char</span> branch_index;</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;          computeBranchIndex (results[result_index], flags[result_index], threshold, branch_index);</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;          branch_indices[result_index] = branch_index;</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;        }</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;      }</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160; </div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00254"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_stats_estimator.html#a34daa4a214600dca0b200580280633af">  254</a></span>&#160;      <a class="code" href="classpcl_1_1_regression_variance_stats_estimator.html#a34daa4a214600dca0b200580280633af">computeBranchIndex</a>(</div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">float</span> result,</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">char</span> flag,</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">float</span> threshold,</div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;        <span class="keywordtype">unsigned</span> <span class="keywordtype">char</span> &amp; branch_index)<span class="keyword"> const</span></div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;<span class="keyword">      </span>{</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;        branch_estimator_-&gt;computeBranchIndex (result, flag, threshold, branch_index);</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;        <span class="comment">//branch_index = (result &gt; threshold) ? 1 : 0;</span></div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;      }</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160; </div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;      <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00271"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_stats_estimator.html#a269548f1ae1f8c96b27ab71657763393">  271</a></span>&#160;      <a class="code" href="classpcl_1_1_regression_variance_stats_estimator.html#a269548f1ae1f8c96b27ab71657763393">computeAndSetNodeStats</a> (</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;        DataSet &amp; data_set,</div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;        std::vector&lt;ExampleIndex&gt; &amp; examples,</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;        std::vector&lt;LabelDataType&gt; &amp; label_data,</div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;        NodeType &amp; node)<span class="keyword"> const</span></div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;<span class="keyword">      </span>{</div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_examples = examples.size ();</div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160; </div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;        LabelDataType sum = 0.0f;</div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;        LabelDataType sqr_sum = 0.0f;</div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> example_index = 0; example_index &lt; num_of_examples; ++example_index)</div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;        {</div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;          <span class="keyword">const</span> LabelDataType label = label_data[example_index];</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160; </div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;          sum += label;</div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;          sqr_sum += label*label;</div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;        }</div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160; </div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;        sum /= num_of_examples;</div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;        sqr_sum /= num_of_examples;</div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160; </div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">float</span> variance = sqr_sum - sum*sum;</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160; </div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;        node.value = sum;</div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;        node.variance = variance;</div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;      }</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160; </div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;      <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00303"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_stats_estimator.html#ab5d5128e679e2d1a252e376cf1bf856d">  303</a></span>&#160;      <a class="code" href="classpcl_1_1_regression_variance_stats_estimator.html#ab5d5128e679e2d1a252e376cf1bf856d">generateCodeForBranchIndexComputation</a> (</div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;        NodeType &amp; node,</div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;        std::ostream &amp; stream)<span class="keyword"> const</span></div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;<span class="keyword">      </span>{</div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;        stream &lt;&lt; <span class="stringliteral">&quot;ERROR: RegressionVarianceStatsEstimator does not implement generateCodeForBranchIndex(...)&quot;</span>;</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;      }</div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160; </div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;      <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00315"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_stats_estimator.html#a11783c9d3b7e5a81b91b17d290ae4950">  315</a></span>&#160;      <a class="code" href="classpcl_1_1_regression_variance_stats_estimator.html#a11783c9d3b7e5a81b91b17d290ae4950">generateCodeForOutput</a> (</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;        NodeType &amp; node,</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;        std::ostream &amp; stream)<span class="keyword"> const</span></div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;<span class="keyword">      </span>{</div>
<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;        stream &lt;&lt; <span class="stringliteral">&quot;ERROR: RegressionVarianceStatsEstimator does not implement generateCodeForBranchIndex(...)&quot;</span>;</div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;      }</div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160; </div>
<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;    <span class="keyword">private</span>:</div>
<div class="line"><a name="l00324"></a><span class="lineno"><a class="line" href="classpcl_1_1_regression_variance_stats_estimator.html#a6aa8a1cbb16354060dd5800d8f331c1d">  324</a></span>&#160;      <a class="code" href="classpcl_1_1_branch_estimator.html">pcl::BranchEstimator</a> * <a class="code" href="classpcl_1_1_regression_variance_stats_estimator.html#a6aa8a1cbb16354060dd5800d8f331c1d">branch_estimator_</a>;</div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;  };</div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160; </div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;}</div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160; </div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;<span class="preprocessor">#endif</span></div>
<div class="ttc" id="aclasspcl_1_1_branch_estimator_html"><div class="ttname"><a href="classpcl_1_1_branch_estimator.html">pcl::BranchEstimator</a></div><div class="ttdoc">Interface for branch estimators.</div><div class="ttdef"><b>Definition:</b> branch_estimator.h:52</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_node_html"><div class="ttname"><a href="classpcl_1_1_regression_variance_node.html">pcl::RegressionVarianceNode</a></div><div class="ttdoc">Node for a regression trees which optimizes variance.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:54</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_node_html_a0a97daa781ba92b787d65caf3c91c3ac"><div class="ttname"><a href="classpcl_1_1_regression_variance_node.html#a0a97daa781ba92b787d65caf3c91c3ac">pcl::RegressionVarianceNode::RegressionVarianceNode</a></div><div class="ttdeci">RegressionVarianceNode()</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:57</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_node_html_a24c9f71bfad5c728bf605f445174ef17"><div class="ttname"><a href="classpcl_1_1_regression_variance_node.html#a24c9f71bfad5c728bf605f445174ef17">pcl::RegressionVarianceNode::serialize</a></div><div class="ttdeci">void serialize(std::ostream &amp;stream) const</div><div class="ttdoc">Serializes the node to the specified stream.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:65</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_node_html_a266146f39a982a8f1c1f167c29e1b63d"><div class="ttname"><a href="classpcl_1_1_regression_variance_node.html#a266146f39a982a8f1c1f167c29e1b63d">pcl::RegressionVarianceNode::variance</a></div><div class="ttdeci">LabelType variance</div><div class="ttdoc">The variance of the labels that ended up at this node during training.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:117</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_node_html_a3463081addd9c9e2237f764e031b6d4e"><div class="ttname"><a href="classpcl_1_1_regression_variance_node.html#a3463081addd9c9e2237f764e031b6d4e">pcl::RegressionVarianceNode::deserialize</a></div><div class="ttdeci">void deserialize(std::istream &amp;stream)</div><div class="ttdoc">Deserializes a node from the specified stream.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:86</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_node_html_a404fad6110f5fed14015839d59c6dca7"><div class="ttname"><a href="classpcl_1_1_regression_variance_node.html#a404fad6110f5fed14015839d59c6dca7">pcl::RegressionVarianceNode::threshold</a></div><div class="ttdeci">float threshold</div><div class="ttdoc">The threshold applied on the feature response.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:112</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_node_html_a4b1410ff0469bde2394b843c70229556"><div class="ttname"><a href="classpcl_1_1_regression_variance_node.html#a4b1410ff0469bde2394b843c70229556">pcl::RegressionVarianceNode::feature</a></div><div class="ttdeci">FeatureType feature</div><div class="ttdoc">The feature associated with the node.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:110</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_node_html_a52ce572a1d9852122bd74c074b76c2e3"><div class="ttname"><a href="classpcl_1_1_regression_variance_node.html#a52ce572a1d9852122bd74c074b76c2e3">pcl::RegressionVarianceNode::value</a></div><div class="ttdeci">LabelType value</div><div class="ttdoc">The label value of this node.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:115</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_node_html_a84b352820a9f306fd303f5fca7792da0"><div class="ttname"><a href="classpcl_1_1_regression_variance_node.html#a84b352820a9f306fd303f5fca7792da0">pcl::RegressionVarianceNode::sub_nodes</a></div><div class="ttdeci">std::vector&lt; RegressionVarianceNode &gt; sub_nodes</div><div class="ttdoc">The child nodes.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:120</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_node_html_abaa10aed19a5efe2a0c836b1c4d0b0cb"><div class="ttname"><a href="classpcl_1_1_regression_variance_node.html#abaa10aed19a5efe2a0c836b1c4d0b0cb">pcl::RegressionVarianceNode::~RegressionVarianceNode</a></div><div class="ttdeci">virtual ~RegressionVarianceNode()</div><div class="ttdoc">Destructor.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:59</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_stats_estimator_html"><div class="ttname"><a href="classpcl_1_1_regression_variance_stats_estimator.html">pcl::RegressionVarianceStatsEstimator</a></div><div class="ttdoc">Statistics estimator for regression trees which optimizes variance.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:127</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_stats_estimator_html_a11783c9d3b7e5a81b91b17d290ae4950"><div class="ttname"><a href="classpcl_1_1_regression_variance_stats_estimator.html#a11783c9d3b7e5a81b91b17d290ae4950">pcl::RegressionVarianceStatsEstimator::generateCodeForOutput</a></div><div class="ttdeci">void generateCodeForOutput(NodeType &amp;node, std::ostream &amp;stream) const</div><div class="ttdoc">Generates code for label output.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:315</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_stats_estimator_html_a269548f1ae1f8c96b27ab71657763393"><div class="ttname"><a href="classpcl_1_1_regression_variance_stats_estimator.html#a269548f1ae1f8c96b27ab71657763393">pcl::RegressionVarianceStatsEstimator::computeAndSetNodeStats</a></div><div class="ttdeci">void computeAndSetNodeStats(DataSet &amp;data_set, std::vector&lt; ExampleIndex &gt; &amp;examples, std::vector&lt; LabelDataType &gt; &amp;label_data, NodeType &amp;node) const</div><div class="ttdoc">Computes and sets the statistics for a node.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:271</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_stats_estimator_html_a34daa4a214600dca0b200580280633af"><div class="ttname"><a href="classpcl_1_1_regression_variance_stats_estimator.html#a34daa4a214600dca0b200580280633af">pcl::RegressionVarianceStatsEstimator::computeBranchIndex</a></div><div class="ttdeci">void computeBranchIndex(const float result, const unsigned char flag, const float threshold, unsigned char &amp;branch_index) const</div><div class="ttdoc">Computes the branch index for the specified result.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:254</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_stats_estimator_html_a3701ce301bd5fbd96aa7ee8ce6c2dc29"><div class="ttname"><a href="classpcl_1_1_regression_variance_stats_estimator.html#a3701ce301bd5fbd96aa7ee8ce6c2dc29">pcl::RegressionVarianceStatsEstimator::getLabelOfNode</a></div><div class="ttdeci">LabelDataType getLabelOfNode(NodeType &amp;node) const</div><div class="ttdoc">Returns the label of the specified node.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:149</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_stats_estimator_html_a5de2e6cbeff519afd171414f66a953aa"><div class="ttname"><a href="classpcl_1_1_regression_variance_stats_estimator.html#a5de2e6cbeff519afd171414f66a953aa">pcl::RegressionVarianceStatsEstimator::computeBranchIndices</a></div><div class="ttdeci">void computeBranchIndices(std::vector&lt; float &gt; &amp;results, std::vector&lt; unsigned char &gt; &amp;flags, const float threshold, std::vector&lt; unsigned char &gt; &amp;branch_indices) const</div><div class="ttdoc">Computes the branch indices for all supplied results.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:229</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_stats_estimator_html_a64941b7d4d7e4edd9262b18e4c36937a"><div class="ttname"><a href="classpcl_1_1_regression_variance_stats_estimator.html#a64941b7d4d7e4edd9262b18e4c36937a">pcl::RegressionVarianceStatsEstimator::~RegressionVarianceStatsEstimator</a></div><div class="ttdeci">virtual ~RegressionVarianceStatsEstimator()</div><div class="ttdoc">Destructor.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:135</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_stats_estimator_html_a6aa8a1cbb16354060dd5800d8f331c1d"><div class="ttname"><a href="classpcl_1_1_regression_variance_stats_estimator.html#a6aa8a1cbb16354060dd5800d8f331c1d">pcl::RegressionVarianceStatsEstimator::branch_estimator_</a></div><div class="ttdeci">pcl::BranchEstimator * branch_estimator_</div><div class="ttdoc">The branch estimator.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:324</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_stats_estimator_html_a73f69f48a8a6593b47b34f93968cd1a9"><div class="ttname"><a href="classpcl_1_1_regression_variance_stats_estimator.html#a73f69f48a8a6593b47b34f93968cd1a9">pcl::RegressionVarianceStatsEstimator::RegressionVarianceStatsEstimator</a></div><div class="ttdeci">RegressionVarianceStatsEstimator(BranchEstimator *branch_estimator)</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:131</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_stats_estimator_html_a7c5c4efbb4ac50525954aae8b8389995"><div class="ttname"><a href="classpcl_1_1_regression_variance_stats_estimator.html#a7c5c4efbb4ac50525954aae8b8389995">pcl::RegressionVarianceStatsEstimator::computeInformationGain</a></div><div class="ttdeci">float computeInformationGain(DataSet &amp;data_set, std::vector&lt; ExampleIndex &gt; &amp;examples, std::vector&lt; LabelDataType &gt; &amp;label_data, std::vector&lt; float &gt; &amp;results, std::vector&lt; unsigned char &gt; &amp;flags, const float threshold) const</div><div class="ttdoc">Computes the information gain obtained by the specified threshold.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:164</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_stats_estimator_html_ab5d5128e679e2d1a252e376cf1bf856d"><div class="ttname"><a href="classpcl_1_1_regression_variance_stats_estimator.html#ab5d5128e679e2d1a252e376cf1bf856d">pcl::RegressionVarianceStatsEstimator::generateCodeForBranchIndexComputation</a></div><div class="ttdeci">void generateCodeForBranchIndexComputation(NodeType &amp;node, std::ostream &amp;stream) const</div><div class="ttdoc">Generates code for branch index computation.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:303</div></div>
<div class="ttc" id="aclasspcl_1_1_regression_variance_stats_estimator_html_ae0a9c3defc64353f7053f4eb63edbc6d"><div class="ttname"><a href="classpcl_1_1_regression_variance_stats_estimator.html#ae0a9c3defc64353f7053f4eb63edbc6d">pcl::RegressionVarianceStatsEstimator::getNumOfBranches</a></div><div class="ttdeci">size_t getNumOfBranches() const</div><div class="ttdoc">Returns the number of branches the corresponding tree has.</div><div class="ttdef"><b>Definition:</b> regression_variance_stats_estimator.h:139</div></div>
<div class="ttc" id="aclasspcl_1_1_stats_estimator_html"><div class="ttname"><a href="classpcl_1_1_stats_estimator.html">pcl::StatsEstimator</a></div><div class="ttdoc">Class interface for gathering statistics for decision tree learning.</div><div class="ttdef"><b>Definition:</b> stats_estimator.h:56</div></div>
<div class="ttc" id="acommon_2include_2pcl_2common_2common_8h_html"><div class="ttname"><a href="common_2include_2pcl_2common_2common_8h.html">common.h</a></div></div>
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